Almost all users look at online ratings and reviews before buying a product, visiting a business, or using a service. These reviews are independent, authored by other users, and thus may convey useful information to the end user. Reviews usually have an overall rating, but most of the times there are sub-texts in the review body that describe certain features/aspects of the product. The majority of web sites rank these reviews either by date, or by overall “helpfulness”. However, different users look for different qualities in a product/business/service. In this work, we try to address this problem by proposing a system that creates personalized rankings of these reviews, tailored to each individual user. We discuss how social data, ratings, and reviews can be combined to create this personalized experience. We present our work-in-progress using the Yelp Challenge dataset and discuss some first findings regarding implementation and scalability.